Research Scientist Intern, Large Language Models (PhD)

Meta; London; On-site; GBP 80,000 - 100,000; Full time

Job summary

An innovative firm is seeking a Research Scientist Intern to contribute to groundbreaking advancements in artificial intelligence. This exciting internship offers the opportunity to work on deep learning, natural language processing, and machine learning projects that can impact the future of technology. Interns will engage in research, develop novel algorithms, and collaborate with experts in the field, all while honing their skills in a dynamic environment. This is a unique chance to make significant contributions and gain experience at the forefront of AI research, with the potential for future academic and professional growth.

Qualifications

• Currently pursuing a PhD in a relevant technical field.
• Experience with deep learning frameworks and programming languages.

Responsibilities

• Perform research to advance intelligent machine technology.
• Develop novel NLP algorithms leveraging deep learning.

Skills

Deep Learning

Natural Language Processing

Machine Learning

Python

C++

C

Java

Reinforcement Learning

Computational Statistics

Applied Mathematics

Education

PhD in Computer Science

PhD in Artificial Intelligence

PhD in Natural Language Processing

Tools

Pytorch

Tensorflow

Job description

Research Scientist Intern, Large Language Models (PhD)

We are committed to advancing the field of artificial intelligence by making fundamental advances in technologies to help interact with and understand our world. We are seeking individuals passionate in areas such as deep learning, computer vision, audio and speech processing, natural language processing, machine learning, reinforcement learning, computational statistics, and applied mathematics. Our interns have an opportunity to make core algorithmic advances and apply their ideas at an unprecedented scale. Our internships are twelve (12) to sixteen (16), or twenty-four (24) weeks long and we have various start dates throughout the year.

Responsibilities

1. Perform research to advance the science and technology of intelligent machines.
2. Develop novel and accurate NLP algorithms and systems, leveraging Deep Learning and Machine Learning on big data resources.
3. Analyze and improve efficiency, scalability, and stability of various deployed systems.
4. Collaborate with researchers and cross-functional partners including communicating research plans, progress, and results.
5. Publish research results and contribute to research that can be applied to Meta product development.

Minimum Qualifications

1. Currently has or is in the process of obtaining a PhD degree in Computer Science, Artificial Intelligence, Natural Language Processing, Speech Recognition, or relevant technical field.
2. Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment.
3. Experience with Python, C++, C, Java or other related languages.
4. Experience with deep learning frameworks such as Pytorch or Tensorflow.
5. Experience building systems based on machine learning, deep learning methods, or natural language processing.

Preferred Qualifications

1. Intent to return to a degree program after the completion of the internship/co-op.
2. Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading workshops or conferences such as NeurIPS, ICLR, ICML, ACL, NAACL, EMNLP, or similar.
3. Experience with ML areas such as Natural Language Processing, Speech, Multimodal Reasoning & Retrieval.
4. Experience manipulating and analyzing complex, high-volume, high-dimensionality data from varying sources.
5. Experience with training deep neural networks for key NLP tasks.
6. Experience with interpreting deep neural networks mechanistically, correlating their observable behavior with properties of model parameters and activations.
7. Demonstrated software engineer experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g. GitHub).
8. Experience working and communicating cross functionally in a team environment.